Cheetah optimization for optimal power management in standalone solar PV systems with EV integration

Abstract This research investigates the application of a novel Cheetah Optimization (CO) algorithm for optimal load allocation in a standalone solar photovoltaic (PV) system integrated with an electric vehicle (EV) battery. Unlike conventional MPPT techniques like Perturb & Observe, Grey Wolf Optimization (GWO), and Flying Squirrel Optimization (FSO), the CO algorithm, inspired by the hunting strategies of cheetahs, effectively balances exploration and exploitation of the solution space, leading to improved convergence and optimal load allocation. Simulation studies, validated by hardware-in-the-loop (HIL) testing using Opal-RT, demonstrated the superior performance of CO. CO achieved a 15% higher peak PV power (reaching 1085 W) compared to the best-performing alternative, with a tracking efficiency exceeding 99.997%. Load power delivery was enhanced by 20%, while the CO algorithm optimized battery charging with a 5% faster charging rate and a higher peak State-of-Charge (SOC). Furthermore, CO exhibited improved system stability with a voltage deflection of only 0.65% and allowable battery voltage deviation maintained within 2%. The system demonstrated low Total Harmonic Distortion (THD): 2.12% for PV power, 3.2% for load power, and 1.96% for battery power, significantly lower than other methods. Statistical analysis, including comparisons of mean values and standard deviations, further confirmed the superiority of CO in terms of energy utilization and system stability. These findings highlight the potential of the CO algorithm as a highly effective and promising solution for optimizing load allocation in standalone solar PV systems with EV battery integration, enabling improved energy utilization, reduced reliance on grid power, and enhanced system reliability.